ArticleInternational journal of statistics in medical research2014
A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit.
Article in International journal of statistics in medical research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A data-driven single substitution to overcome bias from conventional single substitution for LOD missingness in covariates in least squares regression.Journal of applied statistics · 2026Article
- A comparison of computational algorithms for the Bayesian analysis of clinical trials.Clinical trials (London, England) · 2024Article
- Semiparametric analysis of a generalized linear model with multiple covariates subject to detection limits.Statistics in medicine · 2022Article
- A calibrated Bayesian method for the stratified proportional hazards model with missing covariates.Lifetime data analysis · 2022Article
- Challenges of Modeling Outcomes for Surgical Infections: A Word of Caution.Surgical infections · 2021Article
- Cox regression model with randomly censored covariates.Biometrical journal. Biometrische Zeitschrift · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
The research on biomarkers has been limited in its effectiveness because biomarker levels can only be measured within the thresholds of assays and laboratory instruments, a challenge referred to as a detection limit (DL) problem. In this paper, we propose a Bayesian approach to the Cox proportional hazards model with explanatory variables subject to lower, upper, or interval DLs. We demonstrate that by formulating the time-to-event outcome using the Poisson density with counting process notation, implementing the proposed approach in the OpenBUGS and JAGS is straightforward. We have conducted extensive simulations to compare the proposed Bayesian approach to the other four commonly used methods and to evaluate its robustness with respect to the distribution assumption of the biomarkers. The proposed Bayesian approach and other methods were applied to an acute lung injury study, in which a panel of cytokine biomarkers was studied for the biomarkers' association with ventilation-free survival.
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Registered trials
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